Parkinson?s disease (PD), multiple system atrophy parkinsonian type (MSA-P), and progressive supranuclear palsy (PSP) are costly and devastating neurodegenerative diseases. They have overlapping clinical manifestations and diagnosis remains challenging in many cases. Thus far no effective treatments have been developed to meaningfully slow or stop their progression. This is due in part to a lack of tools to objectively measure degeneration in the neural systems affected by each of these diseases. Availability of such tools would assist clinical diagnosis, facilitate selection of appropriate patient groups for trial recruitment, and enable objective measurement of treatment outcomes. Recent MRI studies suggest that disease-specific brain changes can, indeed, be identified in these parkinsonian diseases. The objective of this research is to identify univariable markers and multivariable MRI signatures that capture distinct patterns of neurodegenerative change across neural systems to accurately distinguish these diseases. To accomplish this the investigators use 3 Tesla MRI contrasts sensitive to key features of neurodegeneration to 1) identify structures damaged by PD, MSA-P and PSP and 2) to quantify the extent of damage in each neural system in parkinsonian diseases. Specifically, the investigators use neuromelanin- sensitive MRI to measure neuromelanin loss and quantitative susceptibility mapping (QSM) and R2* imaging to measure iron accumulation in patients with PD, MSA-P, and PSP. Using these contrasts and an innovative region of interest (ROI) selection approach, they reproducibly measure patterns of neurodegenerative change across neural systems. In Aim 1 the investigators use neuromelanin-sensitive MRI to study neuromelanin loss in PD, MSA-P and PSP in to identify univariable disease features that are differentially affected by parkinsonian diseases and may assist distinguishing these conditions. In Aim 2 they use QSM and R2* MRI to study ROIs differentially impacted by PD, MSA-P and PSP to identify iron accumulation biomarkers to help distinguish these diseases. In Aim 3 the investigators apply machine learning classification algorithms to identify multivariable MRI signatures of neurodegenerative change across neural systems to differentiate PD, MSA-P and PSP. Study outputs will be candidate MRI biomarkers and disease signatures. The long term goal of this research is to further develop these outputs for use as clinical diagnostic tools and as biomarkers for subject selection and outcome measurement in clinical trials. Through this career award Dr. Huddleston will gain new skills in MRI methods, data science, and neural systems imaging in parkinsonian diseases. These new skills will enable Dr. Huddleston to design and lead interdisciplinary neuroimaging biomarker studies for Parkinson?s disease and related disorders. His mentor team is comprised of leaders in their fields. This team and the dynamic research environment at Emory University provide the necessary support for Dr. Huddleston to successfully transition to scientific independence.